EmptyDroplets (FDR <= 0.1) + scDblFindersetwd("/media/jacopo/Elements/re_align/MM/PRJNA732205/SAMN19314105/SRR14629339/")
# Load the libraries (from Sarah script + biomart)
library(tidyverse) # packages for data wrangling, visualization etc
library(Seurat) # scRNA-Seq analysis package
library(clustree) # plot of clustering tree
library(ggsignif) # Enrich your 'ggplots' with group-wise comparisons
library(clusterProfiler) #The package implements methods to analyze and visualize functional profiles of gene and gene clusters.
library(org.Hs.eg.db) # Human annotation package neede for clusterProfiler
library(ggrepel) # extra geoms for ggplo2
library(patchwork) #multiplots
library(reticulate)
Load and do the QC for the cellranger data
#list.files(".")
dat <- Read10X(data.dir ="./out/counts_filtered/")
dat <- CreateSeuratObject(dat) # Create the seurat object from the 10x data
kb.initial <- dat@assays[["RNA"]]@counts@Dim[[2]]
cat("Initial number of cells:", kb.initial,
"\nNumber of genes:", dat@assays[["RNA"]]@counts@Dim[[1]])
## Initial number of cells: 3798
## Number of genes: 36601
Empty cells were already filtered, check for % mt RNA and death markers:
# first calculate the mitochondrial percentage for each cell
dat$percent_mt <- PercentageFeatureSet(dat, pattern="^MT.")
# make violin plots
mt_rna = 20
max_counts = 40000
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
VlnPlot(dat, features = c("nCount_RNA", "nFeature_RNA", "percent_mt")) + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot1 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "percent_mt")
plot1 <- plot1 + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot2 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2 <- plot2 + geom_vline(xintercept = max_counts, linetype = "dotted")
plot1
plot2
## cells retained by mt RNA content ( 20 %): 3420
## percentage of retained cells: 90.05 %
## cells retained by counts ( 40000 ): 3224
## percentage of retained cells: 84.89 %
Check the distribution of the cells with low counts and control death markers:
min_counts = 200
hist(dat@meta.data$nCount_RNA, breaks = 100, xlab = "Counts")
hist(dat@meta.data$nCount_RNA, breaks = 1000, xlab = "Counts", xlim = c(0,5000))
hist(dat@meta.data$nCount_RNA, breaks = 10000, xlab = "Counts", xlim = c(0,1000))
abline(v=min_counts, col="red", lty = 3)
The evident peak of cells with < 200 counts could contain dying
cells.
# Subset the dataset to focus only on those cells with low counts
dat.lowcount <- subset(dat, subset = nCount_RNA < min_counts)
# Get the mean of the counts for each gene and sort them decreasing
meanCounts <- rowMeans(GetAssayData(object = dat.lowcount, slot = 'counts'))
meanCounts <- sort(meanCounts, decreasing = T)
# A boxplot can help to observe the distribution of the means
#boxplot(meanCounts)
# Print the most highly expressed genes
head(meanCounts, 30)
## IGLC2 IGHA1 MALAT1 B2M S100A8 MZB1 HLA-B
## 54.8627451 7.8692810 5.3267974 4.5098039 1.3267974 0.7581699 0.6078431
## SSR4 NEAT1 RPLP1 DEFA3 IGKC MT-CO3 S100A9
## 0.5555556 0.5032680 0.4444444 0.4183007 0.3921569 0.3921569 0.3594771
## RPL41 JCHAIN UBC IGLC1 FTL ITM2C HSP90B1
## 0.3333333 0.2941176 0.2810458 0.2745098 0.2549020 0.2483660 0.2418301
## IGLC3 MT-CO2 RPS18 PTMA TMSB4X RPL10 KRTCAP2
## 0.2352941 0.2352941 0.2222222 0.2156863 0.2156863 0.2156863 0.2026144
## MT-CYB TMSB10
## 0.2026144 0.1960784
## cells retained by counts ( 200 ): 3071
## percentage of retained cells: 80.86 %
dir.create("result")
saveRDS(dat, file = "./result/SAMN19314105_clean_QC.Rds")
#Normalize
dat <- NormalizeData(dat)
# Find the first 4000 variabe features
dat <- FindVariableFeatures(dat, selection.method = "vst", nfeatures = 4000)
Set mean expression to 0 and variance across 1 to avoid highly expressed genes drive the forwarding analyses. Since negative expression is meaningless, scaled data are useful only for UMAP and clustering
# scale data, the scaled data are saved in:
# dat[["RNA"]]@scale.data
all.genes <- rownames(dat)
dat <- ScaleData(dat, vars.to.regress = c("percent_mt","nCount_RNA"))
dat <- RunPCA(dat, features = VariableFeatures(object = dat), verbose = F, seed.use = 1)
print(dat[["pca"]], dims = 1:5, nfeatures = 5)
## PC_ 1
## Positive: HBA1, HBA2, HBB, ALAS2, IGHA1
## Negative: STMN1, BIRC5, H2AFZ, TK1, UBE2T
## PC_ 2
## Positive: STMN1, NUSAP1, BIRC5, RRM2, PBK
## Negative: RPS14, RPS27, B2M, RPS18, RPL34
## PC_ 3
## Positive: S100A4, LY86, CXCR4, ARHGDIB, CORO1A
## Negative: MZB1, Z93930.2, XBP1, FKBP11, NEAT1
## PC_ 4
## Positive: Z93930.2, NEAT1, PPP1R10, CDK14, AC104024.1
## Negative: MYDGF, SEC11C, MANF, MZB1, PDIA4
## PC_ 5
## Positive: IGHA2, IGHA1, MZB1, B2M, IGLC3
## Negative: HBB, HBA1, HBA2, HBD, BLVRB
UMAP is a graph-based method of clustering. The first step in this process is to construct a KNN graph based on the euclidean distance in PCA space:
dat <- FindNeighbors(dat, dims = 1:20)
The graph now can be used as input for the function
runUMAP()
dat <- RunUMAP(dat, dims = 1:20, seed.use = 1)
DimPlot(dat, reduction = 'umap', seed = 1)
## QC metrics
## markers